Multiple target tracking with navigation uncertainty

Abstract

The goal of concurrent mapping and localization (CML) is for a mobile robot to build a map of an unknown environment while simultaneously using that map to navigate. CML can be considered as a problem of multiple target tracking (MTT) in the presence of navigation uncertainty. Although data association errors can have a catastrophic e ect on CML performance, previous approaches to CML, such as stochastic mapping (SM), have either ignored the data association problem, matched features by hand, or used a nearest-neighbor approach [4, 2]. We have developed Integrated Mapping and Navigation (IMAN), a multiple hypothesis approach to CML that generalizes SM to incorporate data association uncertainty and expands multiple hypothesis tracking (MHT) to